Machine Learning-Based Phenomapping in Patients with Heart Failure and Secondary Prevention Implantable Cardioverter-Defibrillator Implantation: A Proof-of-Concept Study
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Yu Deng1, Sijing Cheng1, Hao Huang1
1Cardiac Arrhythmia Center, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, 100037 Beijing, China.
Insights
Machine learning identified distinct patient groups for heart failure (HF) patients receiving secondary prophylactic implantable cardioverter-defibrillator (ICD) therapy. This phenomapping approach aids personalized treatment strategies for HF.
Area of Science:
- Cardiology
- Machine Learning
- Medical Informatics
Background:
- Risk stratification is crucial but underexplored in heart failure (HF) patients undergoing secondary prophylactic implantable cardioverter-defibrillator (ICD) implantation.
- Machine learning-based phenomapping offers a novel approach to stratify these heterogeneous patient populations.
Purpose of the Study:
- To evaluate the efficacy of machine learning-based phenomapping using routine clinical data for identifying distinct patient subgroups in HF.
- To assess if these identified subgroups differ in characteristics and prognosis, particularly regarding appropriate ICD shocks and mortality.
Main Methods:
- Utilized hierarchical k-means clustering on factor analysis of mixed data (FAMD) for phenomapping.
- Included 389 patients with chronic HF and ICDs, analyzing 44 baseline variables.
- Validated phenogroups by comparing baseline features and outcomes (appropriate shock, all-cause death).
Main Results:
- Identified three distinct phenogroups with significant differences in demographics, comorbidities, cardiac structure/function, and HF progression.
- Phenogroup 3 showed the worst HF progression, with significantly higher risks for appropriate shocks and all-cause death compared to Phenogroup 1.
- Phenogroup 2 exhibited an increased risk of appropriate shocks but not mortality compared to Phenogroup 1.
Conclusions:
- Machine learning-based phenomapping successfully identifies distinct phenotype subgroups in HF patients receiving secondary prophylactic ICD therapy.
- This novel strategy holds potential for advancing personalized medicine approaches in managing these patients.
Background:
Previous studies have failed to implement risk stratification in patients with heart failure (HF) who are eligible for secondary implantable cardioverter-defibrillator (ICD) implantation. We aimed to evaluate whether machine learning-based phenomapping using routinely available clinical data can identify subgroups that differ in characteristics and prognoses.
Methods:
A total of 389 patients with chronic HF implanted with an ICD were included, and forty-four baseline variables were collected. Phenomapping was performed using hierarchical k-means clustering based on factor analysis of mixed data (FAMD). The utility of phenomapping was validated by comparing the baseline features and outcomes of the first appropriate shock and all-cause death among the phenogroups.
Results:
During a median follow-up of 2.7 years for device interrogation and 5.1 years for survival status, 142 (36.5%) first appropriate shocks and 113 (29.0%) all-cause deaths occurred. The first 12 principal components extracted using the FAMD, explaining 60.5% of the total variability, were left for phenomapping. Three mutually exclusive phenogroups were identified. Phenogroup 1 comprised the oldest patients with ischemic cardiomyopathy; had the highest proportion of diabetes mellitus, hypertension, and hyperlipidemia; and had the most favorable cardiac structure and function among the phenogroups. Phenogroup 2 included the youngest patients, mostly those with non-ischemic cardiomyopathy, who had intermediate heart dimensions and function, and the fewest comorbidities. Phenogroup 3 had the worst HF progression. Kaplan-Meier curves revealed significant differences in the first appropriate shock (p = 0.002) and all-cause death (p 0.001) across the phenogroups. After adjusting for medications in Cox regression, phenogroups 2 and 3 displayed a graded increase in appropriate shock risk (hazard ratio [HR] 1.54, 95% confidence interval [CI] 1.03-2.28, p = 0.033; HR 2.21, 95% CI 1.42-3.43, p 0.001, respectively; p for trend 0.001) compared to phenogroup 1. Regarding mortality risk, phenogroup 3 was associated with an increased risk (HR 2.25, 95% CI 1.45-3.49, p 0.001). In contrast, phenogroup 2 had a risk (p = 0.124) comparable with phenogroup 1.
Conclusions:
Machine-learning-based phenomapping can identify distinct phenotype subgroups in patients with clinically heterogeneous HF with secondary prophylactic ICD therapy. This novel strategy may aid personalized medicine for these patients.
